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brain-structure.py

@@ -29,7 +29,6 @@ _LICENSE = "ODC-By v1.0"
 class BrainStructureConfig(datasets.BuilderConfig):
     """
     Configuration class for the Brain-Structure dataset.
-    You can define multiple configurations if needed (e.g., different subsets).
     """
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
@@ -38,19 +37,17 @@ class BrainStructureConfig(datasets.BuilderConfig):
 class BrainStructure(datasets.GeneratorBasedBuilder):
     """
     A dataset loader for T1 .nii.gz files plus JSON sidecars indicating splits
-    (train, validation, test). Usage example:
+    (train, validation, test).
 
-        ds = load_dataset(
-            "radiata-ai/brain-structure",
-            name="all",
-            split="train",
-            trust_remote_code=True
-        )
+    Examples of how users typically load this dataset:
+
+    >>> from datasets import load_dataset
+    >>> ds_val = load_dataset("radiata-ai/brain-structure", split="validation", trust_remote_code=True)
+    >>> ds_train = load_dataset("./brain-structure", split="train")  # local clone
     """
 
     VERSION = datasets.Version("1.0.0")
 
-    # If you do NOT need multiple configs, you can define just one here:
     BUILDER_CONFIGS = [
         BrainStructureConfig(
             name="all",
@@ -62,7 +59,7 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
 
     def _info(self):
         """
-        Returns DatasetInfo, including feature types and other meta information.
+        Returns DatasetInfo, including the feature schema and other metadata.
         """
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
@@ -75,8 +72,6 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
                         "participant_id": datasets.Value("string"),
                         "session_id": datasets.Value("string"),
                         "study": datasets.Value("string"),
-
-                        # Additional fields from the JSON sidecar
                         "age": datasets.Value("int32"),
                         "sex": datasets.Value("string"),
                         "clinical_diagnosis": datasets.Value("string"),
@@ -100,15 +95,14 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager: datasets.DownloadManager):
         """
-        Creates SplitGenerators for train, validation, and test.
-        No remote download is performed here. Instead, we reference
-        the local directory containing this script.
-        """
-        # Typically, we use dl_manager.download_and_extract(...) for remote data,
-        # but here we assume the data is already in the same repo as this script.
+        Creates SplitGenerators for 'train', 'validation', and 'test'.
+        *No actual download* is done here, so data_dir is simply '.'.
 
-        # Path to the folder containing this script (and presumably the data).
-        data_dir = os.path.abspath(os.path.dirname(__file__))
+        Why '.'? Because whether the dataset is used locally or from the Hub,
+        Hugging Face will place (or reference) files in this same folder context.
+        """
+        data_dir = "."  # The local folder containing subdirectories like IXI/, DLBS/, etc.
+        logger.info(f"BrainStructure: scanning data in {os.path.abspath(data_dir)}")
 
         return [
             datasets.SplitGenerator(
@@ -127,56 +121,55 @@ class BrainStructure(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(self, data_dir, desired_split):
         """
-        Recursively walks data_dir, locates JSON sidecar files, and yields
-        examples that match the specified 'desired_split'.
+        Recursively walks the directory structure, looking for JSON sidecar files
+        ending in '_scandata.json'. For each matching file, yields an example
+        if sidecar["split"] == desired_split.
+
+        The corresponding .nii.gz is located by prefix matching.
         """
         id_ = 0
         for root, dirs, files in os.walk(data_dir):
             for fname in files:
-                # Only consider sidecar files that start with "msub" and end with "_scandata.json"
-                if not (fname.startswith("msub") and fname.endswith("_scandata.json")):
-                    continue
-
-                sidecar_path = os.path.join(root, fname)
-                with open(sidecar_path, "r") as f:
-                    sidecar = json.load(f)
-
-                # if sidecar's "split" matches the desired_split...
-                if sidecar.get("split") == desired_split:
-                    # attempt to find the .nii.gz with the same "msub" prefix
-                    possible_nii_prefix = fname.replace("_scandata.json", "_T1w")
-                    nii_filepath = None
-                    for potential_file in files:
-                        if (potential_file.startswith(possible_nii_prefix) 
-                            and potential_file.endswith(".nii.gz")):
-                            nii_filepath = os.path.join(root, potential_file)
-                            break
-
-                    if not nii_filepath:
-                        logger.warning(f"No corresponding .nii.gz file found for {sidecar_path}")
-                        continue
-
-                    yield id_, {
-                        "id": str(id_),
-                        "nii_filepath": nii_filepath,
-                        "metadata": {
-                            "split": sidecar.get("split", ""),
-                            "participant_id": sidecar.get("participant_id", ""),
-                            "session_id": sidecar.get("session_id", ""),
-                            "study": sidecar.get("study", ""),
-                            "age": sidecar.get("age", 0),
-                            "sex": sidecar.get("sex", ""),
-                            "clinical_diagnosis": sidecar.get("clinical_diagnosis", ""),
-                            "scanner_manufacturer": sidecar.get("scanner_manufacturer", ""),
-                            "scanner_model": sidecar.get("scanner_model", ""),
-                            "field_strength": sidecar.get("field_strength", ""),
-                            "image_quality_rating": float(sidecar.get("image_quality_rating", 0.0)),
-                            "total_intracranial_volume": float(sidecar.get("total_intracranial_volume", 0.0)),
-                            "license": sidecar.get("license", ""),
-                            "website": sidecar.get("website", ""),
-                            "citation": sidecar.get("citation", ""),
-                            "t1_file_name": sidecar.get("t1_file_name", ""),
-                            "radiata_id": sidecar.get("radiata_id", 0),
-                        },
-                    }
-                    id_ += 1
+                # If you only want "msub" files, you can add: if fname.startswith("msub") and ...
+                if fname.endswith("_scandata.json"):
+                    sidecar_path = os.path.join(root, fname)
+                    with open(sidecar_path, "r") as f:
+                        sidecar = json.load(f)
+
+                    if sidecar.get("split") == desired_split:
+                        # Find the .nii.gz prefix
+                        nii_prefix = fname.replace("_scandata.json", "_T1w")
+                        nii_filepath = None
+                        for potential_file in files:
+                            if potential_file.startswith(nii_prefix) and potential_file.endswith(".nii.gz"):
+                                nii_filepath = os.path.join(root, potential_file)
+                                break
+
+                        if not nii_filepath:
+                            logger.warning(f"No corresponding .nii.gz found for {sidecar_path}")
+                            continue
+
+                        yield id_, {
+                            "id": str(id_),
+                            "nii_filepath": nii_filepath,
+                            "metadata": {
+                                "split": sidecar.get("split", ""),
+                                "participant_id": sidecar.get("participant_id", ""),
+                                "session_id": sidecar.get("session_id", ""),
+                                "study": sidecar.get("study", ""),
+                                "age": sidecar.get("age", 0),
+                                "sex": sidecar.get("sex", ""),
+                                "clinical_diagnosis": sidecar.get("clinical_diagnosis", ""),
+                                "scanner_manufacturer": sidecar.get("scanner_manufacturer", ""),
+                                "scanner_model": sidecar.get("scanner_model", ""),
+                                "field_strength": sidecar.get("field_strength", ""),
+                                "image_quality_rating": float(sidecar.get("image_quality_rating", 0.0)),
+                                "total_intracranial_volume": float(sidecar.get("total_intracranial_volume", 0.0)),
+                                "license": sidecar.get("license", ""),
+                                "website": sidecar.get("website", ""),
+                                "citation": sidecar.get("citation", ""),
+                                "t1_file_name": sidecar.get("t1_file_name", ""),
+                                "radiata_id": sidecar.get("radiata_id", 0),
+                            },
+                        }
+                        id_ += 1